Secure Federated Learning for Cognitive Radio Sensing
This addresses security and privacy issues in federated learning for cognitive radio sensing, but it is incremental as it reviews existing approaches without presenting new experimental results.
The paper tackles the problem of reliable and secure spectrum sensing in cognitive radio environments using federated learning, discussing architectures, algorithms, security threats, and countermeasures with illustrative examples and design recommendations.
This paper considers reliable and secure Spectrum Sensing (SS) based on Federated Learning (FL) in the Cognitive Radio (CR) environment. Motivation, architectures, and algorithms of FL in SS are discussed. Security and privacy threats on these algorithms are overviewed, along with possible countermeasures to such attacks. Some illustrative examples are also provided, with design recommendations for FL-based SS in future CRs.